Living organisms developed in nature through the evolution process are equipped with supremely skilled and sophisticated biological functions that cannot be realized with current engineering techniques. Analysis of these mechanisms may lead to not only elucidation of biological functions but also development of a wide variety of novel engineering systems.
From the viewpoint of a scientist approaching the secrets of living organisms and from that of an engineer developing machinery useful for human kind, the members of Biological Systems Engineering laboratory work on a wide variety of projects to analyze the characteristics of biological functions from theoretical and experimental approaches employing engineering techniques aiming to find new principles peculiar to biological systems, and develop novel medical/welfare apparatuses and industrial devices by applying the elucidated principles.
Through such research activities, the students can learn in-depth knowledge about biological systems based on electricity, electronics, systems and information engineering foundation allowing themselves to become creative engineers capable of seeking a new principle and expanding it into new fields.
There are still a lot of unknown functions and mechanisms hidden in the biological system. If we can elucidate and utilize them from engineering standpoint,then there is a possibility of creating new technologies to carve out the future of the 21st century. The Biological Systems Engineering Laboratory categorizes the broad research field of biological systems into five major research themes in order to explore specific research projects under each theme, and further functionally coordinate and fuse each theme to create novel research fields.
We develop novel signal processing algorithms that enablethe interpretation of human motions, intentions, and physiological/psychological states contained in biological signals, such as myoelectric signals, electroencephalograms, and electrocardiograms, as well as create robotic interfaces and medical welfare equipment.
We model human sensory/motor functions from electrical and electronicperspectives based on experimentally measured data, and develop novel movement support systems and next-generation automobile control systems by incorporating modeled human characteristics.
We propose new machine learning algorithms and neural networks based on probabilistic statistical theory and applythese to the development oflearning and control technologiesfor robots, medical welfare equipment, and medical data classificationtechnology.
Focusing on functions such as locomotion generation, sensation, perception, learning, and judgment, we model brain functions from an engineering viewpoint using artificial neural networks. Ultimately, we aim to model and analyze higher brain functions, especially social brain functions that understand the minds of others and live harmoniously, and Kansei that involves nonverbal, unconscious, and intuitive sensibilities. We also develop artificial life form models based on biological knowledge using the constructed brain models.
We are engaged in the research and development of novel medical support systems and medical devices through medicine-engineering collaborations by utilizing electric and electronic systems and information engineering technologies, such as biomechanical analysis technology, biological signal analysis technology, machine learning technology, and biological simulation technology that were developed in the Biological Systems Engineering laboratory.
Our research results have been published in scientific journals, books, conference proceedings, patent, etc.. The numbers of publications the lab produced are shown as follows
(as of December 21, 2024):
Hirokazu Doi, Akira Furui, Rena Ueda, Koji Shimatani, Midori Yamamoto, Akifumi Eguchi, Naoya Sagara, Kenichi Sakurai, Chisato Mori, and Toshio Tsuji
Scientific Reports (accepted, SCI, IF=3.8)
Akira Furui, Ryota Onishi, Tomoyuki Akiyama, and Toshio Tsuji
IEEE Access, Volume: 12, pp.162814-162824, Digital Object Identifier:10.1109/ACCESS.2024.3487637, Date of Publication: 29 October 2024 (SCI, IF=3.4)
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Kouki Kubo, Seiji Hama, Akira Furui, Tomohiko Mizuguchi, Akiko Yanagawa, Akihiko Kandori, Hiroto Sakai, Yutaro Morisako, Yuki Orino, Maho Hamai, Kasumi Fujita, Shigeto Yamawaki, and Toshio Tsuji
Heliyon, Volume 10, Issue 13, e33135, pp.1-13, doi.org/10.1016/j.heliyon.2024.e33135, 14 June 2024. (SCI, IF=3.4)
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Farina Mohamad Yusoff, Masato Kajikawa, Takayuki Yamaji, Shinji Kishimoto, Tatsuya Maruhashi, Ayumu Nakashima, Toshio Tsuji, and Yukihito Higashi
Scientific Reports, volume 14, Article number: 13704, doi.org/10.1038/s41598-024-64118-0, Published online: 14 June 2024. (SCI, IF=4.6)
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Shumma Jomyo, Akira Furui, Tatsuhiko Matsumoto, Tomomi Tsunoda, and Toshio Tsuji
IEEE Sensors Journal, Volume: 24, Issue: 11, pp.17876 – 17884, doi: 10.1109/JSEN.2024.3386333, Date of Publication: 15 April 2024. (SCI, IF=4.3)
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Zu Soh, Saki Maruko, Manaru Fujita, Shin Wakitani, Masayuki Yoshida, and Toshio Tsuji
IEEE Transactions on Instrumentation and Measurement, vol. 73, pp. 1-9, 2024, Art no. 9509209, doi: 10.1109/TIM.2024.3387502. (SCI, IF=5.6)
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Correlation Analysis Between Echinocytosis Stages and Blood Viscosity During Oxygenator Perfusion: An In Vitro Study
Shigeyuki Okahara, Satoshi Miyamoto, Zu Soh, Masaru Yoshino, Hidenobu Takahashi, Hideshi Itoh, and Toshio Tsuji
ASAIO Journal, 70(11), pp.938-945, DOI: 10.1097/MAT.0000000000002214, November 2024. (SCI, IF=3.1)
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Ziqiang Xu, Zu Soh, Yuta Kurota, Yuya Kimura, Harutoyo Hirano, Takafumi Sasaoka, Atsuo Yoshino, and Toshio TsujiScientific Reports, volume 14, Article number: 3383, doi.org/10.1038/s41598-024-53921-4, Published online: 09 February 2024. (SCI, IF=4.6) URLPDF
Satoshi Miyamoto, Zu Soh, Shigeyuki Okahara, Akira Furui, Taiichi Takasaki, Keijiro Katayama, Shinya Takahashi, and Toshio TsujiIEEE Open Journal of Engineering in Medicine and Biology, Volume: 5, pp. 66-74, DOI: 10.1109/OJEMB.2024.3350922, Date of Publication: 08 January 2024. (ESCI, IF=5.8)
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T. Tsuji, P. Morasso, K. Goto, and K. Ito
Biological Cybernetics, Vol.72, pp.475-485, 1995.
T. Tsuji, O. Fukuda, H. Ichinobe, and M. Kaneko
IEEE Transactions on Systems, Man, and Cybernetics-Part C: Applications and Reviews, Vol. 29, No. 1, pp. 60-72, February 1999.
T. Tsuji, N. Bu, M. Kaneko, and O. Fukuda
IEEE Transactions on Neural Networks, Vol.14, No.2, pp.304-316, March 2003.
O. Fukuda, T. Tsuji, M. Kaneko and A. Otsuka
IEEE Transactions on Robotics and Automation, Vol.19, No.2, pp.210-222, April 2003.
H. Matsubara, H. Hirano, H. Hirano, Z. Soh, R. Nakamura, N. Saeki, M. Kawamoto, M. Yoshizumi, A. Yoshino, T. Sasaoka, S. Yamawaki, and T. Tsuji
Scientific Reports, volume 8, Article number: 3091, doi:10.1038/s41598-018-21223-11, Published online: 15 February 2018.
S. Okahara, Z. Soh, S. Miyamoto, H. Takahashi, S. Takahashi, T. Sueda, and T. Tsuji
IEEE Transactions on Biomedical Engineering, Vol.64, No.7, pp. 1503-1512, DOI:10.1109/TBME.2016.2610968, JULY 2017.
H. Hirano, R. Takama, R. Matsumoto, H. Tanaka, H. Hirano, Z. Soh, T. Ukawa, T. Takayanagi, H. Morimoto, R. Nakamura, N. Saeki, H. Hashimoto, S. Matsui, S. Kishimoto, N. Oda, M. Kajikawa, T. Maruhashi, M. Kawamoto, M. Yoshizumi, Y. Higashi, and T. Tsuji
Scientific Reports, volume 8, Article number: 9263, doi:10.1038/s41598-018-27392-3, Published online: 18 June 2018.
Z. Soh, K. Sakamoto , M. Suzuki , Y. Iino, and T. Tsuji
Scientific Reports, volume 8, Article number: 17190, doi:10.1038/s41598-018-35157-1, Published online: 21 November 2018.
A. Furui, H. Hayashi, and T. Tsuji
IEEE Transactions on Biomedical Engineering, DOI: 10.1109/TBME.2019.2895683, Date of Publication: 28 January 2019.
A. Furui, S. Eto, K. Nakagaki, K. Shimada, G. Nakamura, A. Masuda, T. Chin, and T. Tsuji
Science Robotics, Vol. 4, Issue 31, eaaw6339, DOI: 10.1126/scirobotics.eaaw6339, 26 June 2019.
T. Tsuji, S. Nakashima, H. Hayashi, Z. Soh, A. Furui, T. Shibanoki, K. Shima, and K. Shimatani
Scientific Reports, volume 10, Article number: 1422, doi:10.1038/s41598-020-57580-z, Published online: 29 January 2020.
